Chapter 27 Pedogenic Understanding Raster Classification Methodology for Mapping Soils, Powder River Basin, Wyoming, USA

Abstract Vast areas of the earth need new or updated soil survey data, but traditional methods of soil survey are inefficient, expensive and often inaccurate. We developed and tested a methodology that incorporates geographic information systems (GIS), remote sensing and modelling to predict and map soil distribution in the Powder River Basin of Wyoming. Based on conceptual models in which unique soils are the products of unique sets of soil-forming factors, topographic data derived from digital elevation models (DEMs) and Landsat 5 spectral data were selected to represent soil-forming factors. These digital data were analysed using commercially available GIS and image processing software. Unsupervised, supervised and simple knowledge-based classifications were used in the preliminary stage to develop visual representations of soil-landscape patterns and to plan for field data collection. As more was learned about the survey area from data collection, a knowledge-based decision-tree classification model was built and refined. The resulting maps were evaluated qualitatively by local experts and quantitatively using accuracy assessment, and showed good agreement between predicted and observed map units. Continued technological advancements in spatial data and improved GIS and modelling expertise of soil scientists should increase the accuracy and efficiency of the soil survey process.